



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							int:
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					snake_case__	     =DDIMScheduler.from_config(scheduler.config						)

					self.register_modules(unet=_UpperCAmelCase					,   scheduler=_UpperCAmelCase						)






			@torch.no_grad()
			def __call__(     self					,   _UpperCAmelCase = 1					,   _UpperCAmelCase = None					,   _UpperCAmelCase = 0.0					,   _UpperCAmelCase = 50					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "pil"					,   _UpperCAmelCase = True					,   )							->							Union[ImagePipelineOutput, Tuple]:

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size					,   _UpperCAmelCase						):
							snake_case__	     =(
							    batch_size,
							    self.unet.config.in_channels,
							    self.unet.config.sample_size,
							    self.unet.config.sample_size,
							)
					else:
							snake_case__	     =(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) and len(_UpperCAmelCase						) != batch_size:
							raise ValueError(
							    f"""You have passed a list of generators of length {len(_UpperCAmelCase						)}, but requested an effective batch"""
							    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

					snake_case__	     =randn_tensor(_UpperCAmelCase					,   generator=_UpperCAmelCase					,   device=self.device					,   dtype=self.unet.dtype						)

					# set step values
					self.scheduler.set_timesteps(_UpperCAmelCase						)

					for t in self.progress_bar(self.scheduler.timesteps						):
							# 1. predict noise model_output
							snake_case__	     =self.unet(_UpperCAmelCase					,   _UpperCAmelCase						).sample

							# 2. predict previous mean of image x_t-1 and add variance depending on eta
							# eta corresponds to η in paper and should be between [0, 1]
							# do x_t -> x_t-1
							snake_case__	     =self.scheduler.step(
							    _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   eta=_UpperCAmelCase					,   use_clipped_model_output=_UpperCAmelCase					,   generator=_UpperCAmelCase						).prev_sample

					snake_case__	     =(image / 2 + 0.5).clamp(0					,   1						)
					snake_case__	     =image.cpu().permute(0					,   2					,   3					,   1						).numpy()
					if output_type == "pil":
							snake_case__	     =self.numpy_to_pil(_UpperCAmelCase						)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=_UpperCAmelCase						)









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    list[str] | None = None      ,					UpperCamelCase_    :    dict[str, float] | None = None      ,					UpperCamelCase_    :    bool = False      ,					)       ->	tuple[int, float, str]:
		snake_case__	     =cipher_alphabet or [chr(UpperCamelCase_			) for i in range(97      ,					123			)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				snake_case__	     ={
				    'a': 0.0_8_4_9_7,
				    'b': 0.0_1_4_9_2,
				    'c': 0.0_2_2_0_2,
				    'd': 0.0_4_2_5_3,
				    'e': 0.1_1_1_6_2,
				    'f': 0.0_2_2_2_8,
				    'g': 0.0_2_0_1_5,
				    'h': 0.0_6_0_9_4,
				    'i': 0.0_7_5_4_6,
				    'j': 0.0_0_1_5_3,
				    'k': 0.0_1_2_9_2,
				    'l': 0.0_4_0_2_5,
				    'm': 0.0_2_4_0_6,
				    'n': 0.0_6_7_4_9,
				    'o': 0.0_7_5_0_7,
				    'p': 0.0_1_9_2_9,
				    'q': 0.0_0_0_9_5,
				    'r': 0.0_7_5_8_7,
				    's': 0.0_6_3_2_7,
				    't': 0.0_9_3_5_6,
				    'u': 0.0_2_7_5_8,
				    'v': 0.0_0_9_7_8,
				    'w': 0.0_2_5_6_0,
				    'x': 0.0_0_1_5_0,
				    'y': 0.0_1_9_9_4,
				    'z': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				snake_case__	     =frequencies_dict

		if not case_sensitive:
				snake_case__	     =ciphertext.lower()

		# Chi squared statistic values
		snake_case__	     ={}

		# cycle through all of the shifts
		for shift in range(len(UpperCamelCase_			)			):
				snake_case__	     =''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								snake_case__	     =(alphabet_letters.index(letter.lower()			) - shift) % len(
								    UpperCamelCase_			)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				snake_case__	     =0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								snake_case__	     =letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.lower().count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				snake_case__	     =(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(UpperCamelCase_    :    int			) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		snake_case__	     =min(
		    UpperCamelCase_      ,					key=UpperCamelCase_      ,					)

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    snake_case__
		)     ,	(
		    snake_case__
		)     ,	
		)	     =chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)




